This site requires Javascript to be turned on. Please enable Javascript and reload the page.

The Promise and Practice of Teaching Data Literacy in Social Studies: A Companion Site A Taxonomy of Data Visualizations Information can be visualized in multiple ways, from bar graphs to scatterplots, choropleth maps to distribution maps, timelines to time series. Designers can choose from an array of graphical elements such as points, lines, or icons used to represent data, and multiple aesthetic attributes such as color, shape, and size. Furthermore, designers can apply multiple combinations of titles, legends, and explanatory text to provide context for a data visualization. Given the almost dizzying array of data visualizations students may encounter in social studies, it is helpful to place them in categories related to the types of questions they will help us answer. How do students learn with data visualizations? Reading data visualizations in print and online social studies texts can improve students' overall comprehension and quality of reasoning. And there is evidence to suggest that reading data visualizations helps students better understand historical and geographic context, multiple causation, and change over time — all important concepts for students to grasp in social studies subject areas. However, students may face significant challenges in trying to make sense of different kinds of data visualizations. This section provides insight into both benefits and challenges of reading timelines, maps, and graphs and charts. How should students analyze data visualizations in social studies? The challenges that data visualizations present, coupled with their prevalence in social studies texts, standardized assessments, in online social studies resources, and as sources of information in society, suggest that teaching with and about data visualizations in social studies is essential. This module provides guidance for how teachers can support students' data literacy for social studies. Index of Lesson Plans This page contains a list of the minimal manuals and lessons found on this website. Minimal manuals are designed to be adapted to different grade levels and do not have a grade designation associated with them. Lesson plans are organized according to the school level for which they are designed. However, many of these lessons can be adapted for different grade levels.

Patterns

Spatial data visualizations help us see patterns that are invisible to the naked eye...

This page has paths:

Spatial data visualizations help us answer "where" questions. They show us locations, allow us to see patterns, distribution, movements, or relationships, or help us make comparisons. Most of the spatial data visualizations students will encounter in social studies are maps, but not all maps are the same, and not every map fulfills the same function. In addition, some of the spatial data visualizations students might encounter represent humans' efforts to map out stars and planets, caverns and tunnels, or parts of the human body.
Contents of this path:

Contents of this path:

This page has tags:

Spatial data visualizations help us answer "where" questions. They show us locations, allow us to see patterns, distribution, movements, or relationships, or help us make comparisons. Most of the spatial data visualizations students will encounter in social studies are maps, but not all maps are the same, and not every map fulfills the same function. In addition, some of the spatial data visualizations students might encounter represent humans' efforts to map out stars and planets, caverns and tunnels, or parts of the human body.
Contents of this tag:

Contents of this tag:

Choropleth maps use colors, shades, or patterns to represent variables or ranges of variables within a particular geographic area (e.g., county, state, region, nation). This shaded map of French popular education created in 1826 by mathematician and politician Charles Dupin is considered the first choropleth map. It shows the number of inhabitants per male pupil, with the lighter shaded departments sending more boys to school than the darker ones.
Dot maps use equally sized dots, or sometimes equally sized symbols, to show data is distributed. The dot can represent one count or object, or a unit of objects (e.g., one dot=10 houses). By creating perhaps the most famous dot map of all time in 1854, John Snow was able to see where there were clusters of cholera cases in in the Soho district of London, which helped him trace the source to the Broad Street water pump.